Computer aided diagnosis of an image set

ABSTRACT

A method, system, and storage medium for computer aided processing of an image set includes employing a data source, the data source including an image set acquired from X-ray projection imaging, x-ray computed tomography, or x-ray tomosynthesis, defining a region of interest within one or more images from the image set, extracting feature measures from the region of interest, and reporting at least one of the feature measures on the region of interest. The method may be employed for identifying bone fractures, disease, obstruction, or any other medical condition.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 10/063,819, filed on May 15, 2002, pending, which is herebyincorporated by reference in its entirety.

BACKGROUND OF THE INVENTION

This invention generally relates to computer aided detection anddiagnosis (CAD) of an image set. More particularly, this inventionrelates to a method and system for computer aided detection anddiagnosis of dual energy (“DE”) or multiple energy images, as well as ofradiographic images, computed tomography images, and tomosynthesisimages.

The classic radiograph or “X-ray” image is obtained by situating theobject to be imaged between an X-ray emitter and an X-ray detector madeof photographic film.

Emitted X-rays pass through the object to expose the film, and thedegree of exposure at the various points on the film are largelydetermined by the density of the object along the path of the X-rays.

It is now common to utilize solid-state digital X-ray detectors (e.g.,an array of switching elements and photo-sensitive elements such asphotodiodes) in place of film detectors. The charges generated by theX-rays on the various points of the detector are read and processed togenerate a digital image of the object in electronic form, rather thanan analog image on photographic film. Digital imaging is advantageousbecause the image can later be electronically transmitted to otherlocations, subjected to diagnostic algorithms to determine properties ofthe imaged object, and so on.

Dual energy (DE) imaging in digital X-Ray combines information from twosequential exposures at different energy levels, one with a high energyspectrum and the other with a low energy spectrum. With a digital X-raydetector, these two images are acquired sequentially and processed toget two additional images, each representative of attenuation of a giventissue type, for example bone and soft tissue images. A multiple energyimaging system can be built that can be used to further decompose thetissues in an anatomy. A series of images at different energies/kVps(Energy 1, . . . Energy n) can be acquired in a rapid sequence anddecomposed into different tissue types (Tissue 1, . . . Tissue n).

Computed tomography (CT) systems typically include an x-ray sourcecollimated to form a fan beam directed through an object to be imagedand received by an x-ray detector array. The x-ray source, the fan beamand detector array are orientated to lie within the x-y plane of aCartesian coordinate system, termed the “imaging plane”. The x-raysource and detector array may be rotated together on a gantry within theimaging plane, around the imaged object, and hence around the z-axis ofthe Cartesian coordinate system.

The detector array is comprised of detector elements each of whichmeasures the intensity of transmitted radiation along a ray pathprojected from the x-ray source to that particular detector element. Ateach gantry angle a projection is acquired comprised of intensitysignals from each of the detector elements. The gantry is then rotatedto a new gantry angle and the process is repeated to collect a number ofprojections along a number of gantry angles to form a tomographicprojection set. Each acquired tomographic projection set may be storedin numerical form for later computer processing to reconstruct a crosssectional image according to algorithms known in the art. Thereconstructed image may be displayed on a conventional CRT tube,flat-panel thin-film-transistor array, or may be converted to a filmrecord by means of a computer-controlled camera.

The fan beam may be filtered to concentrate the energies of the x-rayradiation into high and low energies. Thus, two projection sets may beacquired, one at high x-ray energy, and one at low x-ray energy, at eachgantry angle. These pairs of projection sets may be taken at each gantryangle, alternating between high and low x-ray energy, such that patientmovement creates minimal problems. Alternatively, each projection setmay be acquired in separate cycles of gantry rotation, such that x-raytube voltage and filtering need not be constantly switched back andforth.

Diagnosis from radiographic images, computed tomography images, andother medical images has traditionally been a visual task. Due to thesubjective nature of the task, the diagnosis is subject to readervariability. In addition, due to the underlying and overlying structuresrelevant to the pathologies of interest, visual assessment can bedifficult. Subtle rib fractures, calcifications, and metastatic bonelesions (metastases) in the chest can be difficult to detect on astandard chest X-ray. As an additional example, only 5-25% of pulmonarynodules are detected today with chest radiographs, but 35-50% arevisible in retrospect. In a CT acquisition, different regions of theimaged object can be composed of different tissues of differingdensities such that the total attenuation (thus CT number and pixelvalue) are the same. These two regions would have identicalrepresentation in the image, and thus be indistinguishable. Dual energyCT offers the ability to discriminate between the two tissue types.Traditionally, this discrimination would still be a visual task.

SUMMARY OF THE INVENTION

The above discussed and other drawbacks and deficiencies are overcome oralleviated by a method for computer aided processing of dual or multipleenergy images including employing a data source, the data sourceincluding a dual or multiple energy image set including a high energyimage, a low energy image, a bone image, and a soft tissue image,defining a region of interest within an image from the dual or multipleenergy image set, extracting feature measures from the region ofinterest, and reporting at least one of the feature measures on theregion of interest.

In another embodiment, a system for computer aided processing of dualenergy images may include a detector generating a first imagerepresentative of photons at a first energy level passing through astructure and a second image representative of photons at a secondenergy level passing through the structure, a memory coupled to thedetector, the memory storing the first image and the second image, aprocessing circuit coupled to the memory, the processing circuitprocessing a dual energy image set including a first decomposed image, asecond decomposed image, a high energy image, and a low energy imagefrom the first image and the second image, storing the dual energy imageset in the memory as a data source, defining a region of interest withinan image from the dual energy image set, and extracting feature measuresfrom the region of interest, and a reporting device coupled to theprocessing circuit, the reporting device reporting at least one of thefeature measures.

In another embodiment, a storage medium encoded with a machine readablecomputer program code including instructions for causing a computer toimplement a method for aiding in processing of dual or multiple energyimages, where the method includes employing a data source, the datasource including a dual or multiple energy image set having a firstdecomposed image, a second decomposed image, a high energy image, and alow energy image, defining a region of interest within an image from thedual or multiple energy image set, extracting feature measures from theregion of interest, and employing a feature selection algorithm on thefeature measures for identifying an optimal set of features.

In another embodiment, a method for detecting bone fractures,calcifications or metastases includes utilizing a bone image from a dualor multiple energy image set, selecting a region of interest within thebone image to search for a calcification, fracture, erosion, ormetastatic bone lesion, segmenting bone from a background of the boneimage, and identifying a candidate region within the bone as a candidatefor a calcification, fracture or metastatic bone lesion.

In another embodiment, a method for detecting lung disease includesutilizing a soft-tissue image from a dual or multiple energy image set,selecting a region of interest within the soft-tissue image to searchfor an indication of disease, segmenting the region of interest from abackground of the soft-tissue image, identifying a candidate regionwithin a bone image which correlates to the region of interest in thesoft-tissue image, extracting features from the candidate region in thebone image, and classifying the region of interest in the soft-tissueimage as a candidate for soft-tissue disease utilizing the featuresextracted from the bone image.

In another embodiment, a method for computer aided processing ofcomputed tomography images includes acquiring an image set of dualenergy computed tomography images, employing a data source, the datasource including the image set, defining a region of interest within animage from the image set, extracting feature measures from the region ofinterest, and reporting at least one of the feature measures on theregion of interest.

In another embodiment, a method for computer aided processing ofvolumetric computed tomography images includes acquiring an image set ofvolumetric computed tomography images from projections taken atdifferent angles of a structure, employing a data source, the datasource including the image set, defining a region of interest within animage from the image set, extracting feature measures from the region ofinterest, and reporting at least one of the feature measures on theregion of interest.

In another embodiment, a method for computer aided processing oftomosynthesis images includes acquiring an image set of tomosynthesisimages, employing a data source, the data source including the imageset, defining a region of interest within an image from the image set,extracting feature measures from the region of interest, and reportingat least one of the feature measures on the region of interest.

The above discussed and other features and advantages of the presentinvention will be appreciated and understood by those skilled in the artfrom the following detailed description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Referring to the exemplary drawings wherein like elements are numberedalike in the several FIGS.:

FIG. 1 is a block diagram of an exemplary X-ray imaging system;

FIG. 2 is a high-level flowchart of an exemplary image acquisition andprocessing process;

FIG. 3 is a flowchart of exemplary image acquisition processing;

FIG. 4 is a flowchart of exemplary image pre-processing;

FIG. 5 is a flowchart of exemplary image post-processing;

FIG. 6 is a flowchart of a computer aided process of detection anddiagnosis of dual energy images;

FIG. 7 is a flowchart of another computer aided process of detection anddiagnosis of dual energy images;

FIG. 8 is flowchart of an exemplary feature selection algorithm for usein the computer aided process of FIGS. 6 and 7;

FIG. 9 is a flowchart of an exemplary classification algorithm for usein the computer aided process of FIGS. 6 and 7;

FIG. 10 is a flowchart of a computer aided process of detectingcalcifications, fractures, erosions, and metastases in a bone image;

FIG. 11 is a flowchart of a computer aided algorithm for use in theprocess of FIG. 10;

FIG. 12 is a flowchart of a computer aided process of detection anddiagnosis of multiple energy images;

FIG. 13 is a signal flow diagram of a system capable of performingpre-reconstruction analysis;

FIG. 14 is a signal flow diagram of a system capable of performingpost-reconstruction analysis;

FIG. 15 is a flowchart of a computer aided process of detection anddiagnosis of dual energy CT images;

FIG. 16 is a flowchart of a computer aided process of detection anddiagnosis of volume CT images;

FIG. 17 is a flowchart of a computer aided process of detection anddiagnosis of dual energy volume CT images;

FIG. 18 is a flowchart of a computer aided process of detection anddiagnosis of tomosynthesis images; and,

FIG. 19 is a flowchart of a computer aided process of detection anddiagnosis of dual energy tomosynthesis images.

DETAILED DESCRIPTION

FIG. 1 illustrates an exemplary X-ray imaging system 100. The imagingsystem 100 includes an X-ray source 102 and a collimator 104, whichsubject structure under examination 106 to X-ray photons. As examples,the X-ray source 102 may be an X-ray tube, and the structure underexamination 106 may be a human patient, test phantom or other inanimateobject under test. The X-ray source 102 is able to generate photons at afirst energy level and at least a second energy level different than thefirst energy level. Multiple, more than two, energy levels are alsowithin the scope of this method and system.

The X-ray imaging system 100 also includes an image sensor 108 coupledto a processing circuit 110. The processing circuit 110 (e.g., amicrocontroller, microprocessor, custom ASIC, or the like) is coupled toa memory 112 and a display 114. The display 114 may include a displaydevice, such as a touch screen monitor with a touch-screen interface. Asis known in the art, the system 100 may include a computer orcomputer-like object which contains the display 114. The computer orcomputer-like object may include a hard disk, or other fixed, highdensity media dives, connected using an appropriate device bus, such asa SCSI bus, an Enhanced IDE bus, a PCI bus, etc., a floppy drive, a tapeor CD ROM drive with tape or CD media, or other removable media devices,such as magneto-optical media, etc., and a mother board. The motherboardincludes, for example, a processor, a RAM, and a ROM, I/O ports whichare used to couple to the image sensor 108, and optional specializedhardware for performing specialized hardware/software functions, such assound processing, image processing, signal processing, neural networkprocessing, etc., a microphone, and a speaker or speakers. Associatedwith the computer or computer-like object may be a keyboard for dataentry, a pointing device such as a mouse, and a mouse pad or digitizingpad. Stored on any one of the above-described storage media (computerreadable media), the system and method include programming forcontrolling both the hardware of the computer and for enabling thecomputer to interact with a human user. Such programming may include,but is not limited to, software for implementation of device drivers,operating systems, and user applications. Such computer readable mediafurther includes programming or software instructions to direct thegeneral purpose computer to performance in accordance with the systemand method. The memory 112 (e.g., including one or more of a hard disk,floppy disk, CDROM, EPROM, and the like) stores a high energy levelimage 116 (e.g., an image read out from the image sensor 108 after110-140 kVp 5 mAs exposure) and a low energy level image 118 (e.g., animage read out after 70 kVp 25 mAs exposure). Processing circuit 110provides an image 120 for display on device 114. As described in furtherdetail herein, the image 120 may be representative of differentstructures (e.g., soft-tissue, bone). The image sensor 108 may be a flatpanel solid state image sensor, for example, although conventional filmimages stored in digital form in the memory 112 may also be processed asdisclosed below as well.

Operation of the system of FIG. 1 will now be described with referenceto FIGS. 2-6 FIG. 2 depicts a high-level flowchart of exemplaryprocessing performed by the system of FIG. 1. The process begins at step10 with image acquisition. An exemplary image acquisition routine isshown in FIG. 3. As shown in FIG. 3, the image acquisition routineincludes a technique optimization step 11 that includes processing suchas automatic selection of acquisition parameters such as kVp (High andLow), mAs, additional filtration (for example, copper or aluminum),timing, etc. The acquisition parameters can be based on variablesprovided by the user (such as patient size) or obtained automatically bythe system (such as variables determined by a low-dose “pre-shot”).Selection of the acquisition parameters may address problems such asresidual structures, lung/heart motion, decomposition artifacts andcontrast.

Once the acquisition parameters are defined, cardiac gating is utilizedat step 12. Cardiac gating is a technique that triggers the acquisitionof images by detector 108 at a specific point in the cardiac cycle. Thisreduces heart-motion artifacts in views that include the heart, as wellas artifacts indirectly related to heart motion such as lung motion.Cardiac gating addresses lung/heart motion artifacts due to heart/aorticpulsatile motion.

The acquisition of two successive x-ray images at high kVp and low kVp,with a minimum time in between, is depicted as steps 13 and 14,respectively. The filtration of collimator 104 may be changed in betweenacquisitions to allow for greater separation in x-ray energies. Detectorcorrections may be applied to both the high energy image and low energyimage at steps 15 and 16, respectively. Such detector corrections areknown in systems employing flat panel detectors and include techniquessuch as bad pixel/line correction, gain map correction, etc., as well ascorrections specific to dual energy imaging such as laggy pixelcorrections.

Referring to FIG. 2, once the acquisition step 10 is completed, flowproceeds to step 20 where the acquired images are pre-processed. FIG. 4is flowchart of an exemplary pre-processing routine. The pre-processingincludes a scatter correction step 22 which may be implemented insoftware and/or hardware. The scatter correction routine may be appliedto each image individually or utilize common information from both thehigh kVp and the low kVp images to reduce scatter. Existing scattercorrection techniques may be used such as hardware solutions includingspecialized anti-scatter grids, and or software solutions usingconvolution-based or deconvolution-based methods. Additionally, softwaretechniques can utilize information from one image to tune parameters forthe other image. Scatter correction addresses decomposition artifactsdue to x-ray scatter.

Once scatter correction is performed, noise reduction is performed atstep 24 where one or more existing noise reduction algorithms areapplied to the high kVp and the low kVp images, either individually orsimultaneously. The noise correction addresses increased noise that mayresult from the DE decomposition. At step 26, registration is performedto reduce motion artifacts by correcting for motion and aligninganatomies between the high kVp and the low kVp images. The registrationalgorithms may be known rigid-body or warping registration routinesapplied to the high kVp and the low kVp images. Alternatively, thetechniques may be iterative and make use of the additional informationin decomposed soft-tissue and bone images developed at step 30. Theregistration processing addresses residual structures in the soft-tissueimage and/or the bone image and lung/heart motion artifacts.

Referring to FIG.2, once the pre-processing step 20 is completed, flowproceeds to step 30 where the acquired images are decomposed to generatea raw soft-tissue image and a raw bone image. A standard image (alsoreferred to as a standard posterior-anterior (PA) image) is also definedbased on the high kVp image. The decomposition may be performed usingknown DE radiography techniques. Such techniques may includelog-subtraction or basis material decomposition to create rawsoft-tissue and raw bone images from the high-energy and low-energyacquisitions. Information from the raw soft-tissue image and raw boneimage may be used in the registration/motion correction step 26. Forexample, edge information and/or artifact location information can bederived from the decomposed images for use in the registration/motioncorrection.

Referring to FIG. 2, once the decomposition step 30 is completed, flowproceeds to step 40 where the acquired images are post-processed. FIG. 5is a flowchart of an exemplary post-processing routine. As shown in FIG.5, the raw soft-tissue image 41 and the raw bone image 42 are subjectedto similar processing. Contrast matching 43 is performed match contrastof structures in raw soft-tissue image 41 and the raw bone image 42 tothe corresponding structures in a standard image. For example, contrastof soft-tissue structures in raw soft-tissue image 41 (e.g., chestimage) is matched to the contrast in the standard PA image. The contrastmatching is performed to facilitate interpretation of the x-ray images.

At 44, one or more noise reduction algorithms may be applied to thesoft-tissue image 41 and the bone image 42. Existing noise reductionalgorithms may be used. The noise reduction addresses noise due to DEdecomposition. At 45, presentation image processing may be performed tothe soft-tissue image 41 and the bone image 42. The presentationprocessing includes processes such as edge enhancement, display windowlevel and window width adjustments for optimal display. The result ofthe post-processing 40 is depicted as processed soft-tissue image 46 andprocessed bone image 47.

Referring to FIG. 2, once the post-processing step 40 is completed, flowproceeds to step 50 where the acquired images are processed for display.

Computer-aided algorithms have the potential of improving accuracy andreproducibility of disease detection when used in conjunction withvisual assessment by radiologists. Computer-aided algorithms can be usedfor detection (presence or absence) or diagnosis (normal or abnormal).The detection or diagnosis is performed based upon knowledge acquired bytraining on a representative sample database. The sample data in thedatabase and the features of the data that the algorithms are trainedare two important aspects of the training process that affect theperformance of CAD algorithms. The accuracy of the CAD algorithmsimproves with improvements on the information it is trained on. Withconventional radiographs, overlying and underlying structures confoundthe relevant information making diagnosis or detection difficult evenfor computerized algorithms. The method and system described hereinaddresses this problem by using dual energy images, in particular, inconjunction with conventional radiographic images for CAD. Inparticular, this method combines information from four images to aidcomputerized detection algorithms.

As shown in FIG. 6 the dual energy CAD system 200 has several partsincluding a data source 210, a region of interest 220, optimal featureselection 230, and classification 240, training 250, and display ofresults 260.

It should be noted here that dual energy CAD 200 may be performed onceby incorporating features from all images 215 or may be performed inparallel. As shown in FIG. 7, the parallel operation for a dual energyCAD 201 would involve performing CAD operations, as described in FIG. 6,individually on each image 216, 217, 218, 219 and combining the resultsof all CAD operations in a synthesis/classification stage 214. That is,the ROI selection 220 can be performed on each image 216, 217,218, and219 to provide a low energy image ROI 221, a high energy image ROI 222,a soft tissue image ROI 223, and a bone image ROI 224. Likewise, theoptimal feature extraction stage 230 can be performed on each image ROI221, 222, 223, and 224 to result in low energy image features 231, highenergy image features 232, soft tissue image features 233, and boneimage features 234. At the synthesis/classification stage 241, theresults of all of the CAD operations can be combined. Thus, FIGS. 6 and7 show two different methods of performing dual energy CAD, howeverother methods are also within the scope of this invention such as theROI selection stage 220 performing in parallel as shown in FIG. 7, butthe feature extraction stage 230 performing on a combined ROI such asshown in FIG. 6. In addition, CAD operations to detect multiplediseases, fractures, or any other medical condition can be performed inseries or parallel.

Referring now to either FIGS. 6 or 7, for the data source 210, data maybe obtained from a combination of one or more sources. Image acquisitionsystem information 212 such as kVp (peak kilovoltage, which determinesthe maximum energy of the X-rays produced, wherein the amount ofradiation produced increases as the square of the kilovoltage), mA (theX-ray tube current is measured in milliamperes, where 1 mA=0.001 A),dose (measured in Roentgen as a unit of radiation exposure, rad as aunit of absorbed dose, and rem as a unit of absorbed dose equivalent),SID (Source to Image Distance), etc., may contribute to the data source210. Patient demographics/symptoms/history 214 such as smoking history,sex age, and clinical symptoms may also be a source for data 210. Dualenergy image sets 215 (high energy image 216, low energy image 217, boneimage 218, soft tissue image 219, or alternatively stated, first andsecond decomposed images in lieu of bone image 218 and soft tissue image219, where first and second decomposed images may include any materialimages including, but not limited to, soft tissue and bone images) arean additional source of data for the data source 210.

On the image-based data 215, a region of interest 220 can be definedfrom which to calculate features. The region of interest 220 can bedefined several ways. For example, the entire image 215 could be used asthe region of interest 220. Alternatively, a part of the image, such asa candidate nodule region in the apical lung field could be selected asthe region of interest 220. The segmentation of the region of interest220 can be performed either manually or automatically. The manualsegmentation may involve displaying the image and a user delineating thearea using, for example, a mouse. An automated segmentation algorithmcan use prior knowledge such as the shape and size to automaticallydelineate the area of interest 220. A semi-automated method which is thecombination of the above two methods may also be used.

The feature selection algorithm 230 is then employed to sort through thecandidate features and select only the useful ones and remove those thatprovide no information or redundant information. With reference to FIG.8, the feature extraction process, or optimal feature extraction 230,involves performing computations on the data sources 210. For example,on the image-based data 215, the region of interest statistics such asshape, size, density, curvature can be computed. On acquisition-based212 and patient-based 214 data, the data 212, 214 themselves may serveas the features. As further shown in FIG. 8, the multiple featuremeasures 270 from the high energy image, low energy image, soft image,and bone images or a combination of those images are extracted, forexample measured features such as shape, size, texture, intensity,gradient, edge strength, location, proximity, histogram, symmetry,eccentricity, orientation, boundaries, moments, fractals, entropy, etc.,patient history such as age, gender, smoking history, and acquisitiondata such as kVp and dose. The term “feature measures” thus refers tofeatures which are computed, features which are measured, and featureswhich just exist. A large number of feature measures are included,however the method ensures that only the features which provide relevantinformation are maintained. Step 272 within the feature selectionalgorithm 230 refers to feature evaluation 272 in terms of its abilityto separate the different classification groups using, for example,distance criteria. Distance criteria will evaluate how well, using aparticular feature, the method can separate the different classes thatare used. Several different distance criteria can be used such asdivergence, Bhattacharya distance, Mahalanobis distance. Thesetechniques are described in “Introduction to Statistical PatternRecognition”, K. Fukanaga, Academic Press, 2^(nd) ed., 1990, which isherein incorporated by reference. Step 274 ranks all the features basedon the distance criteria. That is, the features are ranked based ontheir ability to differentiate between different classes, theirdiscrimination capability. The feature selection algorithm 230 is alsoused to reduce the dimensionality from a practical standpoint, where thecomputation time might be too long if the number of features to computeis large. The dimensionality reduction step 276 refers to how the numberof features are reduced by eliminating correlated features. Extrafeatures which are merely providing the same information as otherfeatures are eliminated. This provides a reduced set of features whichare used by the forward selection step 278 which selects the highestranked features and then adding additional features, based on adescending ranking, until the performance no longer improves. That is,no more features are added when the point is reached where addingadditional features no longer provides any useful information. At thispoint, the output 280 provides an optimal set of features.

Once the features, such as shape, size, density, gradient, edges,texture, etc., are computed as described above in the feature selectionalgorithm 230 and an optimal set of features 280 is produced, apre-trained classification algorithm 240 can be used to classify theregions of interest 220 into benign or malignant nodules,calcifications, fractures or metastases, or whatever classifications areemployed for the particular medical condition involved. With referenceto FIG. 9, the set of features 280 is used as the input to theclassification algorithm 240. In step 282, the normalization of thefeature measures from set 280 is performed with respect to featuremeasures derived from a database of known normal and abnormal cases ofinterest. This is taken from the prior knowledge from training 250. Theprior knowledge from training may contain, for example, examples offeatures of confirmed malignant nodules and examples of features ofconfirmed benign nodules. The training phase 250 may involve, forexample, the computation of several candidate features on known samplesof benign and malignant nodules. Step 284 refers to grouping thenormalized feature measures. Several different methods can be used suchas Bayesian classifiers (an algorithm for supervised learning thatstores a single probabilistic summary for each class and that assumesconditional independence of the attributes given the class), neuralnetworks (which works by creating connections between processingelements whereby the organization and weights of the connectionsdetermine the output; neural networks are effective for predictingevents when the networks have a large database of prior examples to drawon, and are therefore useful in image recognition systems and medicalimaging), rule-based methods (which use conditional statements thattells the system how to react in particular situations), fuzzy logic(which recognizes more than simple true and false values), clusteringtechniques, and similarity measure approach. Such techniques aredescribed in “Fundamentals of Digital Image Processing” by Anil K. Jain,Prentice Hall 1988, herein incorporated by reference. Once thenormalized feature measures have been grouped, then the classificationalgorithm 240 labels the feature clusters in step 286 and outputs instep 288 a display of the output.

Dual-energy techniques enable the acquisition of multiple images forreview by human or machine observers. CAD techniques could operate onone or all of the images 216, 217, 218, and 219, and display the results260 on each image 216, 217, 218, and 219, or synthesize the results fordisplay 260 onto a single image 215. This would provide the benefit ofimproving CAD performance by simplifying the segmentation process, whilenot increasing the quantity of images to be reviewed. This display ofresults 260 forms part of the presentation phase 50 shown in FIG. 2.

Following identification 230 and classification 240 of a suspiciouscandidate region, its location and characteristics should be displayedto the radiologist or reviewer of the image. In non-dual-energy CADapplications this is done through the superposition of a marker, forexample an arrow or circle, near or around the suspicious lesion.Dual-energy CAD affords the ability to display markers for computerdetected (and possibly diagnosed) nodules on any of the four images(high energy image 216, low energy image 217, bone image 218, softtissue image 219). In this way, the reviewer may view only a singleimage 215 upon which is superimposed the results from an array of CADoperations 200. The CAD system 201 synthesizes the results in step 241when the images are processed separately as shown in FIG. 7. Each CADoperation (defined by a unique segmentation (ROI) 220, featureextraction 230, and classification procedure 240 or 241) may berepresented by a unique marker style.

An example of such a dual energy CAD display will be described for lungcancer chest imaging. Let us assume that a patient has a dual-energyexam (as described with reference to FIGS. 1-5) that is then processedwith a dual-energy CAD system 200 or 201. A CAD operation identifies twosuspicious lesions characteristic of malignancy on the soft-tissue image219. On the bone-image 218, a CAD operation identifies a calcifiednodule (indicating a benign process), and a bone lesion. At thesynthesis stage, which may form part of the classification process wheneither or both of the ROI and feature extraction stages apply to eachimage, the classification 240 takes these results and determines thatone of the soft-tissue nodules is the same as the calcified noduleapparent on the bone-image 218. The reviewer would then be presentedwith the high energy image 216, a first image—taken with a technique tomimic what is currently standard practice for single-energy chestradiography. The reviewer would also be presented with a second image,the same image as the first image but with markers indicating theresults of the CAD operations 220, 230, 240 superimposed on the imagedata. This second image could be simultaneously displayed on a secondhard-or soft-copy image display, or toggled with the other images viasoftware on a soft-copy display. Superimposed upon the second image maybe, for example, circles around the suspicious lung nodule classified ashaving characteristics of malignancy, a square around the calcified lungnodules classified as benign, and an arrow pointing to the detected bonelesions. In this manner, the reviewer gets the benefit of theinformation from CAD operations 200 on each image presentedsimultaneously for optimal review.

As another example, the methods 200, 201 may be used in mammography.Dual energy imaging for mammography has been previously employed, suchas described in U.S. Pat. No. 6,173,034 to Chao. Advantageously, themethods 200, 201 may take the results of a dual energy imaging processperformed for mammography and employ the CAD techniques as describedherein. Also, it should be noted that the energies employed inmammography may be as low as 20 kVp as opposed to the energies employedtypically in the above-described chest exam which may be in the range of50-170 kVp. Conventional mammographies are typically 24-30 kVp, and DEmammographies can be 24-30 kVp for the low energy image and 50-80 kVpfor the high energy image, where values are often limited by the x-raytube/generator. For CT mammographies, the energies may be higher, about80 kVp for a conventional single energy image.

These methods 200, 201 improve the performance of computer-aideddetection or diagnosis algorithms by providing input data with overlyingstructures removed. Also, since the imaged anatomy is separated based ontissue type (soft tissue or bone), this algorithm 200 has the potentialof extracting more diagnostic features per anatomy than with standardradiography.

Previous CR (computed radiography) dual-energy images are of rather poorquality and noisy compared to the standard radiology image and thuscomputer-aided algorithms have not been previously employed on suchimages. This system and method 200,201 uses information from high energyimage 216, low-energy image 217, soft-tissue image 219, and bone images218 in addition to acquisition parameters 212 and patient information214. Furthermore, the results can be displayed to the reviewer withoutincreasing the number of images over that of conventional CADtechniques.

The above-described methods 200, 201 can additionally be utilized foridentification of calcifications, bone fractures, bone erosions, andmetastatic bone lesions. By providing a bone image 218 with noover/underlying soft-tissue, DE imaging creates an effective opportunityfor automatic detection and classification of subtle bone fractures,bone erosions, calcifications and metastases that might otherwise bemissed by the standard image reader.

Turning now to FIGS. 10 and 11, a diagrammed example of the methods 200,201 is shown. The method 301 uses a dual energy computer-aideddetection/diagnosis (CAD) algorithm 300 for segmenting the bone from thebackground and detecting/identifying candidate bone regions withpossible calcifications, fractures or metastases. These candidateregions are then classified based on features extracted from thecorresponding complete image set 215 (high-energy 216, low-energy 217,bone 218, and soft-tissue 219). The classification stage not only rulesout what it considers false positives, but can also provide additionalinformation about the fracture or lesion (fracture type, lesion size,etc.) The results are then highlighted on the images for the reader toassess.

As shown in FIG. 11, the first step in a CAD algorithm 300 for detectingcalcifications, bone fractures, bone erosions, and metastases in DEimages 215 requires the selection of the desired area to search, orselection of the region of interest (ROI) 310. In a dual energy chestexam, this would typically include the entire image, but may include asmaller region of interest if a specific region were suspected. Theselection of the region of interest (ROI) 310 can be done manually or byautomated algorithms based on user specifications as described abovewith reference to ROI 220.

Next, segmentation of bone 320 occurs. The purpose of the segmentation320 is to separate the bone from the background (non-bone). Oneembodiment would be a region-growing algorithm. Manual or automatedmethods can be used for initializing region growing. In manual methods,a means is provided for the user to select the seed point(s). Inautomated methods, attributes of the bone such as intensity range,gradient range, shape, size etc. can be used for initializing seedpoints. Another potential segmentation method would involve multi-levelintensity thresholding.

Then, candidate regions can be identified in step 330. One method foridentifying candidate regions is based on an edge detection algorithm.To eliminate noise and false edges, image processing using morphologicalerosion could follow. In addition, to rule out longer lines that aremost likely rib edges, a connectivity algorithm could be applied.Therefore, the remaining image consists of only those edges that arepossible candidates for the calcifications, fractures and metastases.

Candidate regions may then be classified in step 340. The classificationof the remaining candidate regions may comprise a rule-based approach.The rules can be different for identification of calcifications,metastases and fractures. There will preferably be different rules forthe different types of fractures, and different rules for the differentproperties of metastases. For example, for fractures, one might wish toseparate the edges inside the ribs from the edges outside the ribs, asedges inside the ribs are candidates for fractures. Rules could also bebased on size measurements of the line edges.

Remaining candidate regions should then be indicated to the user orreader for inspection in a presentation step, or indication of results350. This could be performed by highlighting areas on the original boneimage, either with arrows, circles, or some other indicator or marker.Additional information such as lesion type or size can also be overlaidon the images.

Referring again to FIG. 10, the indication of results 350 may then beread by a radiologist or clinician in step 360 and this method 301 canbe used to improve the detection of calcifications, subtle ribfractures, subtle bone erosions, and metastatic bone lesions in chestradiography as exemplified by step 370. The detection of such ailmentscan lead to increased benefit to the patient by early detection, leadingto improved patient care by the clinician. The ability to provide a boneimage without over/underlying soft-tissue can also be used to greatlyimprove detection and diagnosis of bone-related pathology. Using thebone image for calcifications, fracture and metastases detection is adiagnostic concept for DE imaging which has not previously beenavailable.

While specific examples including lung cancer chest imaging anddetection of calcifications, bone fractures, bone erosions, andmetastases have been described, it should be understood that the methodsand systems described above could be employed for detecting and/ordiagnosing any medical condition, obstruction, or disease involving anypart of the body.

Also, while DE imaging has been specifically addressed, it is furtherwithin the scope of this invention to employ the above-described methodson multiple energy images. For example, a multiple energy imaging system400 is shown in FIG. 12, which is similar to the DE imaging systems 200,201, and 300 as described above in that it includes a data source 410including image data 415, image acquisition data 412, and patientdemographic data 414, defining or selecting a region of interest 420,optimal feature extraction 430, synthesis/classification 440, andoverlay on image display 460. Also as in the previously described DEimaging systems, prior knowledge from training 450 is applied to theoptimal feature extraction stage 430 and the synthesis/classificationstage 440. Thus, the only distinction between the method 400 and thepreviously described DE methods is the content of the image data 415.That is, while the DE methods utilize a high energy image, a low energyimage, a soft tissue image and a bone image, the multiple energy imagingsystem 400 uses a series of images 413 taken at different energies/kVps(Energy 1 image, Energy 2 image, Energy N image). It should be notedthat “N” denotes an arbitrary number and may change from one imagingprocess to the next. While these images 413 can be acquired in a rapidsequence and decomposed into a bone image 418 and different tissue typeimages, they may also be decomposed into different material images(material 1 image, material 2 image, . . . material N image) which mayor may not include a bone image. Information from one or more of theseimages can be used to detect and diagnose various diseases or medicalconditions. As an example, if a certain disease needs to be detected,regions of interest can be identified and features can be computed onmaterial 2 image and the Energy 1 image. For a different disease type,all the images may be used. As in the DE energy imaging systems, regionof interest selection, optimal feature computation, and classificationmay be performed in series or in parallel on the image data 415. For thepurposes of this specification, it should further be noted that“multiple” energy imaging may encompass dual energy imaging, since twoimages are multiple images.

The CAD system and methods described above may further extend to dual ormultiple energy computed tomography. Referring to FIGS. 13 and 14,pre-reconstruction analysis and post-reconstruction analysis are priorart techniques generally recognized for using dual energy X-ray sourcesin materials analysis. In pre-reconstruction analysis 502, the signalflow is as shown in FIG. 13. The system 100 is typically similar to theone shown in FIG. 1 and has an X-ray source capable of producing a fanbeam at two distinct energy levels (i.e., dual energy). The dataacquisition system 504 gathers signals generated by detector array atdiscrete angular positions of the rotating platform (not shown) andpasses the signals to the pre-processing element 506. The pre-processingelement 506 re-sorts the data it receives from the data acquisitionsystem 504 in order to optimize the sequence for the subsequentmathematical processing. The pre-processing element 506 also correctsthe data from the data acquisition system 504 for detector temperature,intensity of the primary beam, gain and offset, and other deterministicerror factors. Finally, the pre-processing element 506 extracts datacorresponding to high-energy views and routes it to a high energychannel path 508, and routes the data corresponding to low-energy viewsto a low energy path 510. The projection computer 512 receives theprojection data on the high energy path 508 and the low energy path 510and performs Alvarez/Macovski Algorithm processing to produce a firststream of projection data 514 which is dependent on a first parameter ofthe material being scanned and a second stream of projection data 516which is dependent on a second parameter of the material scanned. Thefirst parameter is often the atomic number and the second parameter isoften material density, although other parameters may be selected. Afirst reconstruction computer 518 receives the first stream ofprojection data 514 and generates a CT image from the series ofprojections corresponding to the first material parameter. A secondreconstruction computer 520 receives the second stream of projectiondata 516 and generates a CT image from the series projectionscorresponding to the second material parameter.

In post-reconstruction analysis 503, the signal flow is as shown in FIG.14. As is described herein for pre-processing analysis 502, apre-processing element 506 receives data from a data acquisition system504, performs several operations upon the data, then routes the datacorresponding to high-energy views to a high energy path 508 and routesthe data corresponding to low-energy views to a low energy path 510. Afirst reconstruction computer 518 receives the projection data from thehigh energy path 508 and generates a CT image corresponding to thehigh-energy series of projections. A second reconstruction computer 520receives the projection data from the low-energy path 510 and generatesa CT image corresponding to the low-energy series of projections. Aprojection computer 512 receives the high energy CT data 522 and thelow-energy CT data 524 and performs basis material decomposition toproduct CT data 526 which is dependent on a first parameter of thematerial being scanned and a second stream of projection data 528 whichis dependent on a second parameter of the material scanned.

FIG. 15 shows the method of computer aided detection and diagnosis 202revised for dual energy CT imaging. The method 202 is similar to themethod 200 described with respect to FIG. 6, except that multiple CTimages 1 a, . . . 1N 530 or 534 and multiple CT images 2 a, . . . 2N 532or 536, as described above with respect to either FIGS. 13 or 14,replace high energy image 216 and low energy image 217 to form image set540. It should be understood that “N” may denote any arbitrary numberand need not be the same number as the “N” in FIG. 12. It should also beunderstood that while soft tissue image 219 and bone image 218 areshown, the image data 540 could instead include first decomposed imagesand second decomposed images, as previously described with respect toFIG. 6. Also, it should be understood that the CAD method 201 shown inFIG. 7 could also be revised for dual energy CT.

Also, the embodiment shown in FIG. 15 could be revised for volume CTwhere images of a structure are collected from multiple angles. VolumeCT, or cone-beam CT, is a three-dimensional extension of the morefamiliar two-dimensional fan-beam tomography. In fan-beam tomography, afan collection of X-rays are generated by placing a collimator with along and narrow slot in front of a point X-ray source. A cone-beamfamily of x-rays is made by removing the collimator. This allows thex-rays to diverge from the point x-ray source to form a cone-like solidangle. A divergent line integral data set results when the x-rays, whichpenetrate the object, are collected by a detector located on theopposite side. Cone-beam tomography involves inverting the cone-beamdata set to form an estimate of the density of each point inside theobject.

In current CT scanners, a series of axial images of the object are madeand stacked on top of each other to form the 3D object. In multi-sliceCT, multiple detectors are used to collect multiple slices at a giventime. On the other hand, in cone-beam tomography, the entire data iscollected in parallel and then reconstructed. Therefore, the cone-beamtomography, in theory, improves both the spatial and temporal resolutionof the data. FIG. 16 shows a CAD system 600 which uses Volume CT images1 . . . N for image data 61 S such that data source 610 includes volumeCT images 615, image acquisition data 212, and patient demographic data214. Otherwise, the CAD system is similar to system 200 described withrespect to FIG. 6. Alternatively, the operations 220, 230, 240 may beperformed in parallel on each volume CT image as described with respectto FIG. 7.

In another embodiment, as shown in FIG. 17, a dual energy CAD system 700uses high and low energy Volume CT images 1 a . . . 1N 716 and 2 a . . .2N 717, respectively, as well as soft tissue images 1 . . . N 219 andbone images 1 . . . N 218 (or alternatively first decomposed images andsecond decomposed images) as image data 715, such that data source 710includes image data 715, image acquisition data 212, and patientdemographic data 214. Otherwise, the CAD system 700 is similar to system200 described with respect to FIG. 6. Alternatively, the operations 220,230, 240 may be performed in parallel on each Volume CT image and eachsoft tissue image and bone image as described with respect to FIG. 7.

While Volume CT CAD 600 and DE Volume CT CAD 700 are described in FIGS.16 and 17, it is further contemplated that multiple energy Volume CT CADmay be employed using the methods described with respect to FIG. 12,that is, the method shown in FIG. 17 may be expanded to incorporateadditional energies.

As an alternative embodiment, an imaging mode where limited angle x-raytomosynthesis acquisition is performed and reconstructed may be combinedwith the computer aided detection and diagnosis methods described aboveand as shown in FIG. 18. Tomosynthesis is performed by acquiringmultiple images with a digital detector, i.e. series of low dose imagesused to reconstruct tomography images at any level. Tomosynthesis may beperformed using many different tube motions including linear, circular,elliptical, hypocycloidal, and others. In tomosynthesis, image sequencesare acquired, with typical number of images ranging from 5 to 50. Thus,the imaging portion of this embodiment may be less expensive, althoughnot necessarily preferred, over the CAD CT methods described above. Thetomosynthesis CAD system 800 shown in FIG. 18 is similar to the CT CADsystem shown in FIG. 16 except that the image data 815 includestomosynthesis images 1 . . . N, such that data source 810 includestomosynthesis images 815, image acquisition data 212, and patientdemographic data 214. Other than data source 810, the system 800 issimilar to system 200 described with respect to FIG. 6. Alternatively,the operations 220, 230, 240 may be performed in parallel on eachtomosynthesis image as described with respect to FIG. 7.

In another embodiment, as shown in FIG. 19, a dual energy CAD system 900uses high and low energy tomosynthesis images 1 a . . . 1N 916 and 2 a .. . 2N 917, respectively, as well as soft tissue images 1 . . . N 219and bone images 1 . . . N 218 (or alternatively first decomposed imagesand second decomposed images) as image data 915, such that data source910 includes image data 915, image acquisition data 212, and patientdemographic data 214. Otherwise, the CAD system 900 is similar to system200 described with respect to FIG. 6. Alternatively, the operations 220,230, 240 may be performed in parallel on each tomosynthesis image andeach soft tissue image and bone image as described with respect to FIG.7.

While tomosynthesis CAD 800 and DE tomosynthesis CAD 900 are describedin FIGS. 18 and 19, it is further contemplated that multiple energytomosynthesis CAD may be employed using the methods described withrespect to FIG. 12, that is, the method shown in FIG. 19 may be expandedto incorporate additional energies.

It should be noted that all of the methods described above may beemployed within the imaging system 100, and in particular, may be storedwithin memory 112 and processed by processing circuit 110. It is furtherwithin the scope of this invention that the disclosed methods may beembodied in the form of any computer-implemented processes andapparatuses for practicing those processes. The present invention canalso be embodied in the form of computer program code containinginstructions embodied in tangible media, such as floppy diskettes,CD-ROMs, hard drives, or any other computer-readable storage medium,wherein, when the computer program code is loaded into and executed by acomputer, the computer becomes an apparatus for practicing theinvention. The present invention can also be embodied in the form ofcomputer program code, for example, whether stored in a storage medium,loaded into and/or executed by a computer, or as data signal transmittedwhether a modulated carrier wave or not, over some transmission medium,such as over electrical wiring or cabling, through fiber optics, or viaelectromagnetic radiation, wherein, when the computer program code isloaded into and executed by a computer, the computer becomes anapparatus for practicing the invention. When implemented on ageneral-purpose microprocessor, the computer program code segmentsconfigure the microprocessor to create specific logic circuits.

While the invention has been described with reference to a preferredembodiment, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted forelements thereof without departing from the scope of the invention. Inaddition, many modifications may be made to adapt a particular situationor material to the teachings of the invention without departing from theessential scope thereof. Therefore, it is intended that the inventionnot be limited to the particular embodiment disclosed as the best modecontemplated for carrying out this invention, but that the inventionwill include all embodiments falling within the scope of the appendedclaims. Moreover, the use of the terms first, second, etc. do not denoteany order or importance, but rather the terms first, second, etc. areused to distinguish one element from another.

1. A method for computer aided processing of dual or multiple energyimages acquired using an X-ray source, the method comprising: employinga data source, the data source including a dual or multiple energy imageset including a high energy image, a low energy image, a bone image, anda soft tissue image, each member of the image set being available forprocessing along with each other member of the image set, each member ofthe image set being arranged at the data source in such a manner as toallow the computer aided processing to be performed once, as opposed toparallel operations, by incorporating features from all images of theimage set; defining a region of interest within an image from the dualor multiple energy image set; employing a feature extraction algorithmand extracting feature measures from the region of interest; employing afeature selection algorithm on the region of interest to sort throughfeatures of the region of interest to result in candidate features thatdefine a candidate region of interest, the candidate region of interestbeing a subset of the region of interest, classifying the candidateregion of interest on each image, and subsequently combining results ofall of the computer aided processing operations; and reporting at leastone of the feature measures on the region of interest.
 2. The method ofclaim 1 further comprising acquiring the image set using projectionX-ray radiographic imaging.
 3. The method of claim 1 further comprisingacquiring the image set using x-ray computed tomography (CT), whereinthe dual or multiple energy CT acquisition enables computer aideddiscrimination between different tissue types of differing densitiesfrom different regions of an imaged object.
 4. The method of claim 1further comprising acquiring the image set using digital x-raytomosynthesis.
 5. The method of claim 1 further comprising incorporatingprior knowledge from training for classifying the region of interest. 6.The method of claim 5 wherein incorporating prior knowledge from rainingincludes computing features on known samples of different normal andpathological medical conditions.
 7. The method of claim 6 wherein thefeature selection algorithm sorts through features of the known samples,selects useful features of the known samples, and discards features ofthe known samples which do not provide useful information.
 8. The methodof claim 6 wherein different classification groups are identified forsorting the feature measures, and further wherein the feature selectionalgorithm comprises determining a feature measure's ability to classifythe region of interest into a classification group.
 9. The method ofclaim 8 wherein the feature selection algorithm further comprisesranking each feature measure based on each feature measure's ability toclassify the region of interest into a classification group.
 10. Themethod of claim 9 wherein the feature selection algorithm furthercomprises reducing quantity of feature measures by eliminatingcorrelated features.
 11. The method of claim 9 wherein the featureselection algorithm further comprises selecting a highest ranked featuremeasure and adding additional feature measures in descending order. 12.The method of claim 1 wherein classifying the region of interestcomprises classifying one or more medical conditions.
 13. The method ofclaim 1 wherein the data source further includes at least one of imageacquisition system information and demographic information, symptoms,and history of patient, wherein the image acquisition systeminformation, demographic information, symptoms, and history of patientserve as feature measures in the feature selection algorithm.
 14. Themethod of claim 1 further comprising detecting and diagnosing at leastone medical condition.
 15. The method of claim 1 wherein defining aregion of interest comprises manually selecting a region of interest.16. The method of claim 1 wherein defining a region of interestcomprises utilizing an automated algorithm.
 17. The method of claim 16wherein utilizing an automated algorithm includes inputting userspecifications.
 18. The method of claim 1 comprising defining regions ofinterest and incorporating features from all regions of interest on allimages.
 19. The method of claim 1 wherein reporting at least one of thefeature measures comprises using a marker on a display of each imagewithin the dual or multiple energy image set where the at least onefeature measure is located.
 20. The method of claim 19 furthercomprising displaying a single image which incorporates all markers fromeach image within the dual or multiple energy image set.
 21. A methodfor detecting bone fractures, erosions, calcifications or metastasesusing an X-ray source, the method comprising: employing a data source,the data source including a dual or multiple energy image set, the imageset comprising a high energy image, a low energy image, a bone image,and a soft tissue image, each member of the image set being availablefor processing along with each other member of the image set, eachmember of the image set being arranged at the data source in such amanner as to allow processing of the image set to be performed once byincorporating features from all images of the image set; utilizing abone image from a dual or multiple energy image set; selecting a regionof interest within the bane image to search for a calcification,fracture or metastatic bone lesion; segmenting bone from a background ofthe bone image; identifying a candidate region within the bone as acandidate for a calcification, fracture, erosion, or metastatic bonelesion, the candidate region being a subset of the region of interest;and classifying an identified candidate region using a computer aidedrule based approach, wherein different rules apply for calcifications,metastases, erosions, and fractures, and for different types offractures and different properties of metastases.
 22. The method ofclaim 21 wherein rules are based on size measurements of line edges ofthe identified candidate region.
 23. The method of claim 21 whereinsegmenting bone comprises utilizing a region growing algorithm.
 24. Themethod of claim 23 wherein the region growing algorithm is manuallyinitialized by having a user select a seed point.
 25. The method ofclaim 23 wherein the region growing algorithm is automaticallyinitialized by utilizing bone attributes to select a seed point.
 26. Themethod of claim 21 wherein segmenting bone comprises multi-levelintensity thresholding.
 27. The method of claim 21 wherein identifying acandidate region comprises utilizing an edge detection algorithm. 28.The method of claim 27 wherein image processing using morphologicalerosion is used for eliminating noise and false edges.
 29. The method ofclaim 27 wherein rib edges are eliminated using a connectivityalgorithm.
 30. The method of claim 21 further comprising indicating thecandidate region on a display.
 31. The method of claim 30 whereinindicating the candidate region comprises placing a marker on the boneimage indicative of a classification of the candidate region.
 32. Amethod for detecting lung disease using an X-ray source, the methodcomprising: employing a data source, the data source including a dual ormultiple energy image set, the image set comprising a high energy image,a low energy image, a bone image, and a soft tissue image, each memberof the image set being available for processing along with each othermember of the image set, each member of the image set being arranged atthe data source in such a manner as to allow processing of the image setto be performed once by incorporating features from all images of theimage set; utilizing a soft-tissue image from a dual or multiple energyimage set; selecting a region of interest within the soft-tissue imageto search for an indication of disease; segmenting the region ofinterest from a background of the soft-tissue image; identifying acandidate region within a bone image which correlates to the region ofinterest in the soft-tissue image; extracting features from thecandidate region in the bone image; and, classifying the region ofinterest in the soft-tissue image as a candidate for soft-tissue diseaseutilizing the features extracted from the bone image, the classifyingcomprising using a computer aided rule based approach, wherein differentrules apply for different medical conditions, and different rules areused for the soft-tissue and bone-images.
 33. The method of claim 32,further comprising identifying a solitary pulmonary nodule or lesionsand wherein if the features extracted from the bone-image are indicativeof calcification of the nodule, then the method further comprisingutilizing the bone-image calcification features to classify the regionof interest in the soft-tissue image as indicatively benign.
 34. Themethod of claim 32 further comprising reporting at least one of thefeatures using a marker on a display of each image within the dual ormultiple energy image set where the at least one feature is located anddisplaying a single image which incorporates all markers from each imagewithin the dual or multiple energy image set.
 35. The method of claim 34further comprising displaying a single image which incorporates markersuniquely indicative of results from the soft-tissue image that have beenfurther classified based on results from the bone-image.
 36. The methodof claim 3, wherein acquiring the image set comprises acquiring an imageset of volumetric computed tomography images using cone-beam tomography.37. The method of claim 1, wherein the feature selection algorithmcomprises: reducing a quantity of feature measures by eliminatingcorrelated features, thereby eliminating extra features that provide thesame information as other features, resulting in a reduced set offeature measures; and using the reduced set of feature measures,selecting a highest ranked feature measure, and adding additionalfeature measures, based on a descending ranking, until the adding of theadditional feature measures no longer provides additional usefulinformation.
 38. The method of claim 1, wherein the feature selectionalgorithm sorts through candidate features, selects useful ones of thecandidate features, and removes those that provide no information orredundant information.
 39. The method of claim 1, wherein: the reportingcomprises reporting the high energy image, low energy image, or both,with structures from the bone image, soft tissue image, or both,removed.